Do You Train Your Chatbot, Or Vice Versa?

In an age where conversations with artificial intelligence have become commonplace, a fascinating dynamic is emerging: as we shape our AI companions, they simultaneously shape us. This reciprocal relationship raises uncomfortable questions about the nature of human-machine interaction and its long-term effects on our cognition and behavior.

The Dance Of Mutual Adaptation

When you interact with an AI chatbot, you’re engaging in a subtle dance of influence. Your queries, responses, and preferences gradually mold the system to better align with your needs. Modern language models adapt to user preferences through both explicit feedback mechanisms and implicit pattern recognition. They learn your communication style, memorize your preferences, and gradually tailor their responses to match your expectations.

Yet while we’re training our digital companions, something equally significant is happening in reverse. Our interactions with these systems are subtly reshaping our own communication patterns, thought processes, and even expectations of human conversation.

Our relationships with AI systems have begun to rewire our expectations of human interaction. We’re adapting to conversations with immediate responses, perfect understanding, and flawless recall — creating unrealistic expectations when we engage with humans, who naturally communicate with pauses, misunderstandings, and imperfect memory. A 2023 study in Nature Human Behaviour found that prolonged interaction with AI conversational agents significantly altered participants’ communication patterns and expectations in subsequent human interactions. (It also found that online overexposure to our own opinions nurtures polarization).

The Cognitive Mirror Effect

This bidirectional influence creates a cognitive mirror effect – a self-reinforcing cycle where our digital interactions create a reflection of ourselves that becomes increasingly accurate yet potentially more narrow over time.

The phenomenon bears striking similarities to what psychologists call the “echo chamber effect” in social media. Just as recommendation algorithms can lead users into ever more specialized content niches that reinforce existing beliefs, AI conversation partners can inadvertently amplify our existing thought patterns and communication styles. Research on algorithmic personalization has explored this effect in recommendation systems, showing how it can lead to progressively narrower content exposure. The findings suggest that similar narrowing might occur in conversational AI systems that optimize for user satisfaction through personalization. Our affinity for generative AI systems could inadvertently reinforce our cognitive biases and thought patterns over extended interaction periods.

The Neuroplasticity Connection

The parallel to neuroplasticity — our brain’s ability to reorganize itself by forming new neural connections — is striking. When we repeatedly engage in specific thought patterns or behaviors, we strengthen the neural pathways associated with them, making those patterns more likely to recur in the future. The Hebbian principle in neuroscience states that neurons firing together tend to wire together. This principle suggests that repeated patterns of neural activity become increasingly entrenched, making habit formation both powerful and challenging to reverse. Human software shapes human hardware, and vice versa. Neuroimaging studies how repeated behaviors create measurable changes in neural pathways, strengthening connections associated with those specific activities.

Similarly, in our interactions with AI, we may be creating external versions of our own neural pathways. As we train our AI companions to respond in ways we find satisfying, we simultaneously train ourselves to communicate in ways that elicit those satisfying responses. This creates a feedback loop that grows stronger with each interaction. Studies have shown that regular digital interaction patterns can form habitual behaviors that mirror addiction-like neural pathways. This suggests that our interaction styles with AI systems may become progressively ever more automatic and less conscious over time. Prolonged AI usage impacts neural plasticity, and the brain adapts to technological interfaces in ways that can become self-reinforcing.

A Self-Fulfilling Prophecy

This reciprocal relationship creates a mental self-fulfilling prophecy. Our expectations shape our interactions with AI, which then shape the AI’s responses, which further reinforce our expectations. Over time, this cycle can lead to increasingly predictable and deliberately limited exchanges.

The twofold risk is that AI systems develop biases from our data, while our own thinking is shaped by these (biased) systems, which are optimized for user engagement rather than personal growth. The more we are exposed to our own opinions and ways of thought, the more comfortably we settle into them. The old saying “garbage in, garbage out” might thus take a terrible revenge as we are becoming less and less inclined to verify and cross-check the validity of our own assumptions.

Rather than expanding our cognitive horizons, these interactions inadvertently narrow them. We might find ourselves in comfortable but limited conversational loops with our AI companions, each reinforcing the patterns of the other.

Breaking The Loop: The Four A’s Approach

How, then, do we maintain beneficial relationships with AI systems while avoiding these potential pitfalls? A practical framework emerges around four key principles:

1. Awareness

The first step is simply recognizing that this bidirectional influence exists. Being mindful of how our interactions with AI systems might be shaping our own communication patterns allows us to make more conscious choices about these relationships.

Pay attention to how you communicate with AI compared to humans. Notice if your queries have become more directive or if you’ve adapted your language to better “fit” what the system understands. This awareness alone can help prevent unconscious narrowing of communication patterns.

2. Appreciation

Rather than viewing this mutual influence as entirely problematic, we can appreciate its potential benefits. The ability of AI systems to adapt to our needs can make them more effective tools, while our own adaptations may include positive developments such as more precise communication or clearer articulation of thoughts.

Take time to reflect on what you’ve gained from your AI interactions. Perhaps you’ve become more explicit in your requests, more precise in your language, or more structured in your thinking—skills that can transfer positively to human interactions.

3. Acceptance

Some degree of mutual adaptation is inevitable in any relationship, including those with AI systems. Accepting this reality while maintaining healthy boundaries allows us to engage productively without undue concern.

Understand that perfect neutrality in AI interaction is neither possible nor necessarily desirable. Instead, focus on ensuring the adaptations occurring are ones you consciously choose rather than passively accept.

4. Accountability

Ultimately, we bear responsibility for how we engage with technology and what we allow it to reinforce in ourselves. Taking an active role in these relationships—deliberately varying our interaction styles, occasionally challenging our AI companions with novel queries, and regularly stepping back to reflect on these exchanges—helps ensure they remain enriching rather than limiting.

Consider periodically “resetting” your interaction patterns with AI systems. Try new approaches, ask different types of questions, or deliberately engage in styles of conversation you wouldn’t typically pursue. This practice helps prevent the formation of overly rigid interaction habits.

Who Trains Who?

The question “Do you train your chatbot, or vice versa?” doesn’t have a simple answer. The truth lies in recognizing that both processes occur simultaneously, creating a complex ecosystem of mutual influence. By approaching these relationships with awareness, appreciation, acceptance, and accountability, we can harness their benefits while mitigating potential limitations.

In this evolving dance with our digital companions, maintaining our cognitive flexibility may be the most important skill of all. Just as healthy neuroplasticity requires varied experiences and challenges, healthy relationships with AI may require us to consciously vary our interactions, remaining mindful of how these systems both reflect and shape our thinking.

As we move forward in this new frontier of human-AI interaction, the most valuable approach may be one of conscious co-evolution – allowing ourselves and our digital companions to grow together, while ensuring that growth expands rather than constrains our human potential.